UAV and satellite-based multispectral/hyperspectral imaging for nutrient deficiency mapping
Proximal sensors (chlorophyll meters, fluorescence) for real-time leaf health assessment
Integration into DSS for variable-rate irrigation and fertilization prescriptions
Remote Sensing of LAI
Definition and importance of Leaf Area Index (LAI) in crop growth modeling
Satellite platforms (Sentinel-2, Landsat 8) and UAV-borne multispectral sensors for LAI mapping
Spectral indices (NDVI, EVI, Red-Edge) and narrow-band algorithms tailored to canopy structure
Retrieval methods: empirical regression models vs. physical radiative transfer model inversion
Spatial resolution (10–30 m vs. sub-meter) and temporal revisit trade-offs for monitoring dynamics
Integration of LAI products into precision management: stress detection, variable-rate irrigation and fertilization
graph TD
A[Data Acquisition: Satellite] --> B[Preprocessing: Atmospheric Correction]
B --> C[Compute Spectral Indices NDVI, Red-Edge]
C --> D{Retrieval Method}
D -->|Empirical| E[Regression Model]
D -->|Physical| F[RTM Inversion]
E --> G[LAI Map]
F --> G[LAI Map]
G --> H[Precision Application: Scouting, VRF]
Grain Quality Sensors In-Field
On-combine near-infrared (NIR) sensors for real-time grain protein and moisture measurement
GPS-referenced data capture enabling spatial protein mapping across paddocks
Development of variable-rate N application prescriptions from protein maps to target low-protein zones
Integration with yield monitors and GIS for overlaying protein, yield, and soil data layers
Utilisation of 4R framework in sensor-driven protein management to optimise N source, rate, timing, and placement
Adaptive in-season adjustments reducing N in waterlogged or high-protein areas to mitigate environmental risk
Spectral Data & Vegetation Interaction
Calculation of key vegetation indices (NDVI, NDRE, PRI) from multispectral/UAV sensors
Correlation of spectral reflectance with canopy biomass and chlorophyll content
Spatiotemporal analysis of crop stress and water uptake dynamics
Calibration using ground truth from neutron-probe and soil-core volumetric data
Role of Yield Monitors in Precision Ag
Real-time mass flow and moisture sensors capture instantaneous yield data
GPS-stamped yield maps enable high-resolution spatial analysis of productivity
Calibration protocols ensure sensor accuracy across varying crop conditions
Integration with prescription files allows dynamic variable-rate nutrient application
Temporal yield trends facilitate delineation of management zones and decision making
Continuous feedback loop supports long-term optimization of input use and ROI
Importance of Yield Map Interpretation
Spatial vs temporal variability in yield patterns
Impact of variability pattern (patch uniformity vs mosaic complexity)
Measurement scales: within-field mapping to whole-farm surveys; timing across growth stages/seasons
Role of soil texture: sand/silt/clay distribution; EC and gamma-radiometric sensing for texture zoning
Influence of soil structure: aggregation, porosity, degradation impacts on root growth, water availability, and erosion
Guiding SSCM through yield map interpretation to optimize input allocation
graph LR
A[Yield Data Collection] --> B[Yield Map Analysis]
B --> C[Variability Pattern Identification]
C --> D[Management Zone Delineation]
D --> E[Site-Specific Input Application]
Yield Map Data Cleaning
Seven years of georeferenced yield data (wheat, maize, sunflower, sorghum) aggregated
Spatial structure quantified through semi-variogram fitting for each dataset
Simple kriging interpolation to a unified 30 m grid across all years
Standardization of yield values and detection of outliers via statistical thresholds
Alignment and stacking of annual yields into a multispectral yield cube
Quality-control checks to ensure spatial and temporal consistency
Calibrating Yield Monitors
Pre-season sensor inspection: clean sensors, inspect cables and slip rings
Mass flow calibration: collect reference weights across multiple loads to establish sensor-voltage curve
Ground speed validation: compare GNSS-derived speed with radar/encoder readings
Hybrid clustering frameworks combining EC, elevation, NDVI, canopy volume, and yield to define nutrient, irrigation, and pest management zones.
Spatio-temporal Bayesian models integrating multi-source covariates for predictive mapping of nitrogen requirements and yield variability.
Case studies: citrus productivity zones, wheat nitrogen management, and irrigation zoning via soil water capacity and multispectral imagery.
Abiotic Stress Patterns
Spatial mapping of soil moisture and nutrient variability to delineate abiotic stress hotspots
Remote sensing indices (NDVI, thermal IR) for early detection of drought and heat stress
Variable-rate irrigation and fertilization based on stress zone boundaries and temporal crop demand
Conservation structures (grassed waterways, contour furrows) to reduce erosion under extreme precipitation events
Precision residue harvesting and soil-specific tillage to improve moisture retention and mitigate compaction
Riparian buffers and constructed wetlands for off-field interception of nutrient and sediment runoff
Routine for Crop Scouting
Establish grid-based scouting paths (30–60 m spacing)
Use GPS-enabled devices for georeferenced field data
Monitor pest, disease, and nutrient status with proximal sensors and UAV imagery
Record soil moisture and temperature using in-field probes
Integrate NDVI and thermal imaging for early stress detection
Upload and sync data to GIS for spatial analysis and intervention mapping
graph LR
A[Plan Grid Sampling Paths] --> B[Field Data Collection]
B --> C[Data Upload & QC]
C --> D[Geostatistical Analysis]
D --> E[Site-Specific Interventions]
E --> F[Review & Adjust]
Monitoring & Data Logging Routine
Pre-start of cropping season (ECa soil clusters)
In-season NDVI acquisition from UAV and satellite platforms for canopy chlorophyll mapping (target scouting)
Harvest Yield Maps (end-results)
Integration into spatio-temporal database enabling dynamic MZ delineation and trend analysis